Overview

Dataset statistics

Number of variables10
Number of observations18905
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory80.0 B

Variable types

Numeric10

Alerts

fLength is highly overall correlated with fWidth and 7 other fieldsHigh correlation
fWidth is highly overall correlated with fLength and 6 other fieldsHigh correlation
fSize is highly overall correlated with fLength and 5 other fieldsHigh correlation
fConc is highly overall correlated with fLength and 5 other fieldsHigh correlation
fConc1 is highly overall correlated with fLength and 5 other fieldsHigh correlation
fAsym is highly overall correlated with fLength and 3 other fieldsHigh correlation
fM3Long is highly overall correlated with fLength and 6 other fieldsHigh correlation
fM3Trans is highly overall correlated with fLength and 6 other fieldsHigh correlation
fDist is highly overall correlated with fLengthHigh correlation

Reproduction

Analysis started2022-11-26 18:15:35.096689
Analysis finished2022-11-26 18:16:04.687175
Duration29.59 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

fLength
Real number (ℝ)

Distinct18643
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.104755 × 10-17
Minimum-2.3431512
Maximum2.3784554
Zeros0
Zeros (%)0.0%
Negative10248
Negative (%)54.2%
Memory size147.8 KiB
2022-11-26T13:16:04.887490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.3431512
5-th percentile-1.3839779
Q1-0.85513557
median-0.15697001
Q30.88130152
95-th percentile1.6724231
Maximum2.3784554
Range4.7216066
Interquartile range (IQR)1.7364371

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)-4.7512725 × 1016
Kurtosis-1.0562185
Mean-2.104755 × 10-17
Median Absolute Deviation (MAD)0.8298754
Skewness0.26230987
Sum-7.1054274 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:05.214008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.193649306 3
 
< 0.1%
-0.8259122325 3
 
< 0.1%
-0.7003107252 3
 
< 0.1%
-0.4505813804 2
 
< 0.1%
-1.637577731 2
 
< 0.1%
0.7006335982 2
 
< 0.1%
-1.125574545 2
 
< 0.1%
-1.065228614 2
 
< 0.1%
-1.073571335 2
 
< 0.1%
-0.8439460404 2
 
< 0.1%
Other values (18633) 18882
99.9%
ValueCountFrequency (%)
-2.343151153 1
< 0.1%
-2.095361104 1
< 0.1%
-2.082720012 1
< 0.1%
-2.025867318 1
< 0.1%
-2.011274824 1
< 0.1%
-2.011217709 1
< 0.1%
-1.990932174 1
< 0.1%
-1.983329968 1
< 0.1%
-1.981122842 1
< 0.1%
-1.97311359 1
< 0.1%
ValueCountFrequency (%)
2.378455446 1
< 0.1%
2.328689858 1
< 0.1%
2.317025106 1
< 0.1%
2.316802111 1
< 0.1%
2.316283148 1
< 0.1%
2.312788512 1
< 0.1%
2.312125241 1
< 0.1%
2.304373261 1
< 0.1%
2.297782161 1
< 0.1%
2.294338196 1
< 0.1%

fWidth
Real number (ℝ)

Distinct18200
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.104755 × 10-17
Minimum-3.2673558
Maximum2.8053668
Zeros0
Zeros (%)0.0%
Negative9668
Negative (%)51.1%
Memory size147.8 KiB
2022-11-26T13:16:05.526640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3.2673558
5-th percentile-1.5594383
Q1-0.76069203
median-0.028759215
Q30.66951867
95-th percentile1.7859447
Maximum2.8053668
Range6.0727226
Interquartile range (IQR)1.4302107

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)-4.7512725 × 1016
Kurtosis0.11705984
Mean-2.104755 × 10-17
Median Absolute Deviation (MAD)0.71575757
Skewness0.017422911
Sum-1.9895197 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:05.855392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.267355783 98
 
0.5%
-0.9448223949 4
 
< 0.1%
-0.186715686 3
 
< 0.1%
-3.266613194 3
 
< 0.1%
-0.2964929244 3
 
< 0.1%
0.3001450041 3
 
< 0.1%
-3.266586675 3
 
< 0.1%
-0.9865575548 3
 
< 0.1%
-1.068595233 3
 
< 0.1%
-0.6139020032 3
 
< 0.1%
Other values (18190) 18779
99.3%
ValueCountFrequency (%)
-3.267355783 98
0.5%
-3.267329261 3
 
< 0.1%
-3.267302739 1
 
< 0.1%
-3.267196651 1
 
< 0.1%
-3.266851875 1
 
< 0.1%
-3.266692754 2
 
< 0.1%
-3.266666234 2
 
< 0.1%
-3.266639714 1
 
< 0.1%
-3.266613194 3
 
< 0.1%
-3.266586675 3
 
< 0.1%
ValueCountFrequency (%)
2.805366816 1
< 0.1%
2.749289517 1
< 0.1%
2.732636687 1
< 0.1%
2.68616122 1
< 0.1%
2.656883809 1
< 0.1%
2.655742391 1
< 0.1%
2.65218269 1
< 0.1%
2.646544734 1
< 0.1%
2.623696302 1
< 0.1%
2.619004281 1
< 0.1%

fSize
Real number (ℝ)

Distinct7228
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.5966219
Maximum3.254562
Zeros0
Zeros (%)0.0%
Negative9511
Negative (%)50.3%
Memory size147.8 KiB
2022-11-26T13:16:06.435098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5966219
5-th percentile-1.6522849
Q1-0.72677371
median-0.0082942865
Q30.73234606
95-th percentile1.6342199
Maximum3.254562
Range5.8511839
Interquartile range (IQR)1.4591198

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)nan
Kurtosis-0.5534986
Mean0
Median Absolute Deviation (MAD)0.72931434
Skewness0.049544457
Sum-3.479439 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:06.770263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.806784528 27
 
0.1%
-2.075394378 24
 
0.1%
-1.886717187 24
 
0.1%
-1.875092558 23
 
0.1%
-1.242830888 22
 
0.1%
-1.840682893 22
 
0.1%
-1.863485051 22
 
0.1%
-1.322544543 21
 
0.1%
-1.731949059 20
 
0.1%
-1.129913649 20
 
0.1%
Other values (7218) 18680
98.8%
ValueCountFrequency (%)
-2.596621877 1
 
< 0.1%
-2.574989117 1
 
< 0.1%
-2.400548012 1
 
< 0.1%
-2.376656267 1
 
< 0.1%
-2.359737901 1
 
< 0.1%
-2.343233827 2
 
< 0.1%
-2.327142033 3
 
< 0.1%
-2.311078413 4
< 0.1%
-2.295043009 1
 
< 0.1%
-2.279035865 8
< 0.1%
ValueCountFrequency (%)
3.254562007 1
< 0.1%
3.133115491 1
< 0.1%
3.104952773 1
< 0.1%
2.987217688 1
< 0.1%
2.98562531 1
< 0.1%
2.971977901 1
< 0.1%
2.933823006 1
< 0.1%
2.920461079 1
< 0.1%
2.891714314 1
< 0.1%
2.841785982 1
< 0.1%

fConc
Real number (ℝ)

Distinct6410
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4054343 × 10-17
Minimum-2.5034954
Maximum2.2741159
Zeros0
Zeros (%)0.0%
Negative9542
Negative (%)50.5%
Memory size147.8 KiB
2022-11-26T13:16:07.129005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.5034954
5-th percentile-1.5838601
Q1-0.77878135
median-0.016579962
Q30.75679596
95-th percentile1.7085657
Maximum2.2741159
Range4.7776113
Interquartile range (IQR)1.5355773

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)4.1573634 × 1016
Kurtosis-0.78243567
Mean2.4054343 × 10-17
Median Absolute Deviation (MAD)0.76782958
Skewness0.041114631
Sum-7.327472 × 10-14
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:07.479363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.179444113 15
 
0.1%
0.3045373006 12
 
0.1%
-0.3625543211 12
 
0.1%
0.2426818028 12
 
0.1%
-0.8798189335 11
 
0.1%
-0.9074610265 11
 
0.1%
0.7405962796 11
 
0.1%
1.243198112 11
 
0.1%
-1.083748895 11
 
0.1%
-0.477831128 10
 
0.1%
Other values (6400) 18789
99.4%
ValueCountFrequency (%)
-2.503495446 1
< 0.1%
-2.501805766 1
< 0.1%
-2.498427095 1
< 0.1%
-2.496738104 2
< 0.1%
-2.480704153 1
< 0.1%
-2.477331231 1
< 0.1%
-2.469745525 1
< 0.1%
-2.455429712 1
< 0.1%
-2.448698633 1
< 0.1%
-2.440289987 1
< 0.1%
ValueCountFrequency (%)
2.274115875 1
< 0.1%
2.267994877 1
< 0.1%
2.260164963 1
< 0.1%
2.245500285 1
< 0.1%
2.224980534 1
< 0.1%
2.222239477 1
< 0.1%
2.220182893 1
< 0.1%
2.215038466 1
< 0.1%
2.210919865 1
< 0.1%
2.210233166 1
< 0.1%

fConc1
Real number (ℝ)

Distinct4421
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0
Minimum-2.584808
Maximum2.8859839
Zeros0
Zeros (%)0.0%
Negative9513
Negative (%)50.3%
Memory size147.8 KiB
2022-11-26T13:16:07.767902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.584808
5-th percentile-1.5888409
Q1-0.77525208
median-0.0083587269
Q30.7553292
95-th percentile1.6411697
Maximum2.8859839
Range5.4707919
Interquartile range (IQR)1.5305813

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)nan
Kurtosis-0.69572237
Mean0
Median Absolute Deviation (MAD)0.76565558
Skewness0.054116071
Sum1.0309531 × 10-12
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:08.111148image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.03276857538 18
 
0.1%
-0.03379028615 16
 
0.1%
0.1506741259 16
 
0.1%
0.02785234752 16
 
0.1%
0.1920861001 15
 
0.1%
0.2663712373 15
 
0.1%
-0.4222706436 14
 
0.1%
-0.7826850077 14
 
0.1%
-0.2094880872 14
 
0.1%
-0.8250070702 14
 
0.1%
Other values (4411) 18753
99.2%
ValueCountFrequency (%)
-2.584808022 1
< 0.1%
-2.576958318 1
< 0.1%
-2.572251247 1
< 0.1%
-2.565978365 1
< 0.1%
-2.558142429 1
< 0.1%
-2.515927766 1
< 0.1%
-2.511247621 1
< 0.1%
-2.476990127 1
< 0.1%
-2.47543563 1
< 0.1%
-2.470773531 1
< 0.1%
ValueCountFrequency (%)
2.885983914 1
< 0.1%
2.880791104 1
< 0.1%
2.74479941 1
< 0.1%
2.718054136 1
< 0.1%
2.684871029 1
< 0.1%
2.679018756 1
< 0.1%
2.670453052 1
< 0.1%
2.660516403 1
< 0.1%
2.652371597 1
< 0.1%
2.643306079 1
< 0.1%

fAsym
Real number (ℝ)

Distinct18704
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0203786 × 10-18
Minimum-5.7316741
Maximum15.029573
Zeros0
Zeros (%)0.0%
Negative8436
Negative (%)44.6%
Memory size147.8 KiB
2022-11-26T13:16:08.485015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-5.7316741
5-th percentile-1.690573
Q1-0.35105719
median0.071528091
Q30.44546239
95-th percentile1.2807947
Maximum15.029573
Range20.761247
Interquartile range (IQR)0.79651957

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)1.1086302 × 1017
Kurtosis15.852353
Mean9.0203786 × 10-18
Median Absolute Deviation (MAD)0.39324453
Skewness0.79159439
Sum2.7000624 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:08.776236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.001177878665 40
 
0.2%
-0.001179654674 7
 
< 0.1%
0.1577796271 3
 
< 0.1%
-0.01015936808 3
 
< 0.1%
-0.02731954581 3
 
< 0.1%
0.09348664777 2
 
< 0.1%
0.4021517943 2
 
< 0.1%
-0.256589993 2
 
< 0.1%
-0.02298736663 2
 
< 0.1%
-0.4401707008 2
 
< 0.1%
Other values (18694) 18839
99.7%
ValueCountFrequency (%)
-5.731674114 1
< 0.1%
-5.648756671 1
< 0.1%
-4.934148252 1
< 0.1%
-4.924860627 1
< 0.1%
-4.894717914 1
< 0.1%
-4.782825517 1
< 0.1%
-4.725123291 1
< 0.1%
-4.622202034 1
< 0.1%
-4.614803942 1
< 0.1%
-4.576306202 1
< 0.1%
ValueCountFrequency (%)
15.02957275 1
< 0.1%
11.92997521 1
< 0.1%
11.68005876 1
< 0.1%
11.08455684 1
< 0.1%
10.75422865 1
< 0.1%
9.881588146 1
< 0.1%
9.860388669 1
< 0.1%
9.805833014 1
< 0.1%
9.692835301 1
< 0.1%
9.350927683 1
< 0.1%

fM3Long
Real number (ℝ)

Distinct18693
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2027171 × 10-17
Minimum-4.782438
Maximum6.4323174
Zeros0
Zeros (%)0.0%
Negative9496
Negative (%)50.2%
Memory size147.8 KiB
2022-11-26T13:16:09.078573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.782438
5-th percentile-1.6364744
Q1-0.54884487
median-0.0024162656
Q30.45052926
95-th percentile1.628958
Maximum6.4323174
Range11.214755
Interquartile range (IQR)0.99937413

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)8.3147268 × 1016
Kurtosis3.0830499
Mean1.2027171 × 10-17
Median Absolute Deviation (MAD)0.51647471
Skewness0.13119955
Sum1.4210855 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:09.521203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.3102698495 39
 
0.2%
-0.3102717741 4
 
< 0.1%
0.01305761714 3
 
< 0.1%
0.918095124 2
 
< 0.1%
-0.6775045701 2
 
< 0.1%
-0.09910727376 2
 
< 0.1%
-0.129573547 2
 
< 0.1%
0.5561049939 2
 
< 0.1%
0.1023270482 2
 
< 0.1%
0.09308335227 2
 
< 0.1%
Other values (18683) 18845
99.7%
ValueCountFrequency (%)
-4.782438016 1
< 0.1%
-4.634831164 1
< 0.1%
-4.400186772 1
< 0.1%
-4.355342953 1
< 0.1%
-4.2914438 1
< 0.1%
-4.289826862 1
< 0.1%
-4.259735658 1
< 0.1%
-4.228552713 1
< 0.1%
-4.227302533 1
< 0.1%
-4.222657282 1
< 0.1%
ValueCountFrequency (%)
6.432317439 1
< 0.1%
6.196931276 1
< 0.1%
6.073427334 1
< 0.1%
6.023647978 1
< 0.1%
5.890567287 1
< 0.1%
5.739643676 1
< 0.1%
5.729236824 1
< 0.1%
5.707797395 1
< 0.1%
5.671230442 1
< 0.1%
5.271187496 1
< 0.1%

fM3Trans
Real number (ℝ)

Distinct18390
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8592462 × 10-18
Minimum-10.266684
Maximum8.3808096
Zeros0
Zeros (%)0.0%
Negative9422
Negative (%)49.8%
Memory size147.8 KiB
2022-11-26T13:16:09.880741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-10.266684
5-th percentile-1.2524278
Q1-0.53018251
median0.030232135
Q30.51890538
95-th percentile1.2763514
Maximum8.3808096
Range18.647493
Interquartile range (IQR)1.0490879

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)1.4579247 × 1017
Kurtosis8.6831381
Mean6.8592462 × 10-18
Median Absolute Deviation (MAD)0.52412199
Skewness-0.029809738
Sum6.3060668 × 10-14
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:10.232585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.005875017674 59
 
0.3%
-0.005879833775 24
 
0.1%
-0.005870201572 18
 
0.1%
0.5289900271 3
 
< 0.1%
-0.4398347064 3
 
< 0.1%
-0.2687911611 3
 
< 0.1%
0.3925445136 2
 
< 0.1%
0.6747940135 2
 
< 0.1%
-0.4794594582 2
 
< 0.1%
0.5605282741 2
 
< 0.1%
Other values (18380) 18787
99.4%
ValueCountFrequency (%)
-10.26668353 1
< 0.1%
-8.156972766 1
< 0.1%
-7.422112907 1
< 0.1%
-7.071888043 1
< 0.1%
-7.048235285 1
< 0.1%
-6.715827387 1
< 0.1%
-6.677895376 1
< 0.1%
-6.660063491 1
< 0.1%
-6.596824984 1
< 0.1%
-6.560672759 1
< 0.1%
ValueCountFrequency (%)
8.380809571 1
< 0.1%
7.960134016 1
< 0.1%
7.618915517 1
< 0.1%
7.23218336 1
< 0.1%
6.725914091 1
< 0.1%
6.511986153 1
< 0.1%
6.20523895 1
< 0.1%
6.195958995 1
< 0.1%
6.157483688 1
< 0.1%
6.125150476 1
< 0.1%

fAlpha
Real number (ℝ)

Distinct17981
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.4054343 × 10-17
Minimum-1.3837512
Maximum1.7452451
Zeros0
Zeros (%)0.0%
Negative10132
Negative (%)53.6%
Memory size147.8 KiB
2022-11-26T13:16:10.595887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1.3837512
5-th percentile-1.3075352
Q1-0.94865345
median-0.15459512
Q30.93599943
95-th percentile1.6134944
Maximum1.7452451
Range3.1289963
Interquartile range (IQR)1.8846529

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)-4.1573634 × 1016
Kurtosis-1.3612351
Mean-2.4054343 × 10-17
Median Absolute Deviation (MAD)0.89764231
Skewness0.24439549
Sum0
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:10.966733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.383734643 7
 
< 0.1%
-1.383751216 5
 
< 0.1%
-1.362584703 4
 
< 0.1%
-1.165203125 4
 
< 0.1%
-1.278055755 4
 
< 0.1%
-1.317593469 4
 
< 0.1%
-1.351872333 4
 
< 0.1%
-1.160547081 4
 
< 0.1%
1.745245079 4
 
< 0.1%
-1.109110388 4
 
< 0.1%
Other values (17971) 18861
99.8%
ValueCountFrequency (%)
-1.383751216 5
< 0.1%
-1.383734643 7
< 0.1%
-1.383726356 2
 
< 0.1%
-1.38366835 1
 
< 0.1%
-1.383494337 1
 
< 0.1%
-1.383287186 1
 
< 0.1%
-1.383038617 1
 
< 0.1%
-1.383005475 1
 
< 0.1%
-1.382947478 1
 
< 0.1%
-1.382897767 1
 
< 0.1%
ValueCountFrequency (%)
1.745245079 4
< 0.1%
1.744972871 1
 
< 0.1%
1.744677688 1
 
< 0.1%
1.744618373 1
 
< 0.1%
1.744608937 1
 
< 0.1%
1.744205792 1
 
< 0.1%
1.744105998 1
 
< 0.1%
1.744014288 1
 
< 0.1%
1.743999452 1
 
< 0.1%
1.743953594 1
 
< 0.1%

fDist
Real number (ℝ)

Distinct18437
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6081514 × 10-17
Minimum-2.8062079
Maximum3.6189664
Zeros0
Zeros (%)0.0%
Negative9319
Negative (%)49.3%
Memory size147.8 KiB
2022-11-26T13:16:11.360879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-2.8062079
5-th percentile-1.7202497
Q1-0.67227763
median0.018927017
Q30.6514375
95-th percentile1.7008484
Maximum3.6189664
Range6.4251744
Interquartile range (IQR)1.3237151

Descriptive statistics

Standard deviation1.0000264
Coefficient of variation (CV)-2.7715756 × 1016
Kurtosis-0.22245602
Mean-3.6081514 × 10-17
Median Absolute Deviation (MAD)0.6590954
Skewness0.014227636
Sum-6.3238303 × 10-13
Variance1.0000529
MonotonicityNot monotonic
2022-11-26T13:16:11.692957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.25970421 3
 
< 0.1%
0.06536233674 3
 
< 0.1%
-1.043071084 3
 
< 0.1%
0.7220808152 3
 
< 0.1%
-0.04879151063 3
 
< 0.1%
1.328861988 3
 
< 0.1%
-0.06128598892 3
 
< 0.1%
0.3390530649 3
 
< 0.1%
-0.06104109727 3
 
< 0.1%
-1.284646969 3
 
< 0.1%
Other values (18427) 18875
99.8%
ValueCountFrequency (%)
-2.806207905 1
< 0.1%
-2.739069014 1
< 0.1%
-2.738324716 1
< 0.1%
-2.73663162 1
< 0.1%
-2.735910978 1
< 0.1%
-2.723036508 1
< 0.1%
-2.721130307 1
< 0.1%
-2.682272461 1
< 0.1%
-2.620230007 1
< 0.1%
-2.605933067 1
< 0.1%
ValueCountFrequency (%)
3.618966446 1
< 0.1%
3.29763808 1
< 0.1%
3.125933329 1
< 0.1%
3.119793374 1
< 0.1%
3.119202712 1
< 0.1%
3.093341239 1
< 0.1%
3.076141493 1
< 0.1%
2.991338071 1
< 0.1%
2.990850009 1
< 0.1%
2.975393378 1
< 0.1%

Interactions

2022-11-26T13:16:00.868838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:37.084248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:39.913201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:42.750393image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:45.741077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:48.008707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:50.316330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:52.621979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:55.310828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:57.929780image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:01.114560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:37.323770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:40.164032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:43.023550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:45.937958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:48.218253image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:50.540052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:52.857297image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:55.571913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:58.163715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:01.375703image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:37.674465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:40.486190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:43.342647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:46.161732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:48.452781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:50.774095image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:53.125534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:55.825603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:58.468101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:01.650862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:38.004517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:40.818530image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:43.601487image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:46.401710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:48.692082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:51.016613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:53.370399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:56.067455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:58.703134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:01.908652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:38.278467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:41.151923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:43.854711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:46.609298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:48.912503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:51.229647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:53.602846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:56.319463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:58.952328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:02.181551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:38.532698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:41.415324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:44.089642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:46.820208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:49.148434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:51.452504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:53.862303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:56.595221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:59.204109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:02.437526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:38.822827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:41.678616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:44.340937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:47.053659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:49.363993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:51.683414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:54.099885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:56.851486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:59.579187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:02.723321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:39.090128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:41.957834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:44.604342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:47.264128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:49.614498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:51.910395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:54.537497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:57.123693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:59.925385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:03.066857image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:39.384144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:42.216514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:44.850690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:47.509180image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:49.860317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:52.146240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:54.806302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:57.406054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:00.252786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:03.448521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:39.663661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:42.480224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:45.072224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:47.773742image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:50.101067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:52.391726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:55.070158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:15:57.668556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-26T13:16:00.552020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-26T13:16:11.951496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-26T13:16:12.297209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-26T13:16:12.600534image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-26T13:16:12.913728image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-26T13:16:13.258107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-26T13:16:03.886886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-26T13:16:04.414896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDist
0-0.591513-0.169122-0.2509910.1981550.0098080.5142630.139969-0.4023710.781995-1.561882
1-0.436424-0.784861-0.6040180.8786261.3763840.4866360.179488-0.487519-0.8859440.197982
21.8148322.4599562.046496-2.299895-2.2997522.406983-1.415642-2.2149781.5561540.856828
3-0.888940-1.150230-1.1618621.2403191.4666270.504778-0.432749-0.351402-0.597081-1.043071
40.9772631.0224280.834954-0.242601-0.145218-0.0982960.2841771.037004-1.0142202.051910
50.4201670.3886770.369145-0.735784-0.7076200.9763850.6219230.464740-1.0937960.622187
60.305149-0.0036860.610752-0.660943-0.4985950.1534800.5021490.501620-1.0032720.378699
7-0.707933-0.473837-0.5069270.3672220.1958130.5518310.106350-0.147146-1.3169400.609964
81.2828801.5462452.149091-1.968361-1.9945562.2617771.6849012.044835-0.9985670.749869
90.250920-0.273733-0.431988-0.113913-0.0604840.4573990.638288-0.328626-0.776113-1.257056
fLengthfWidthfSizefConcfConc1fAsymfM3LongfM3TransfAlphafDist
18895-0.389292-0.958639-0.7355000.5813360.668239-0.457574-0.6988920.4010271.428082-0.985434
188961.0515131.5082781.416808-1.286127-1.273268-0.6577070.878312-1.466499-0.2555571.523528
18897-0.423915-0.4574160.1908590.3323810.015836-0.284936-0.8028050.526585-0.535624-1.289682
188981.9194022.0359211.599227-1.694740-1.5958854.2108471.901650-3.0757891.6351011.185090
188990.115558-0.0039200.203335-0.428646-0.348488-0.970849-0.916892-0.1821841.5786270.452820
18900-1.044786-0.917372-0.3284581.1194231.4737980.277301-0.0815000.132470-1.187218-1.189215
18901-0.583083-1.699046-1.3995420.9000540.7056790.698277-0.047270-0.1487881.7013470.740207
189020.9826831.5704351.277126-1.466975-1.762544-0.1643240.571491-0.4636730.4546190.853451
189031.5287382.0397561.970208-1.836181-1.5719620.103019-1.843937-3.1362701.6715732.646845
189041.9410041.6881050.914457-0.429298-0.470678-2.432844-2.8627961.4930581.1049101.048742